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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Mathvision : Jurnal Matematika Range : Jurnal Pendidikan Matematika Jubindo: Jurnal Ilmu Pendidikan Bahasa dan Sastra Indonesia Journal of Research in Mathematics Trends and Technology Biocaster : Jurnal Kajian Biologi IC Tech: Majalah Ilmiah Parameter: Jurnal Matematika, Statistika dan Terapannya PADIMAS: Jurnal Pengabdian Masyarakat Journal on Mathematics Education IC Tech: Majalah Ilmiah Media Pendidikan Matematika
Miko Purnomo
Universitas Timor, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Estimation of Path Coefficient Parameter Based on The Best RMSEA Value in Structural Equation Modeling Weighted Least Square Simarmata, Justin Eduardo; Mone, Ferdinandus; Chrisinta , Debora; Purnomo, Miko; Matute, Alejandro Jr. V.
RANGE: Jurnal Pendidikan Matematika Vol. 7 No. 2 (2026): Range Januari 2026
Publisher : Pendidikan Matematika UNIMOR

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32938/jpm.v7i2.10324

Abstract

Structural Equation Modeling (SEM) is a statistical approach widely used to analyze causal relationships between latent and observed variables. A key issue in SEM lies in selecting an appropriate parameter estimation method, as it strongly affects the accuracy and interpretation of results. Among the most common estimation techniques are Maximum Likelihood (ML) and Weighted Least Squares (WLS). This study aims to compare the performance of ML and WLS in estimating path coefficients within SEM analysis. Using simulated data generated with the simulateData() function from a predefined structural model, three scenarios are examined with sample sizes of 500 and 1000. Data transformation procedures are applied to ensure consistency before model testing. Each SEM model is then estimated using both ML and WLS, and the results are evaluated through Root Mean Square Error of Approximation (RMSEA) values obtained from 100 replications. Findings indicate that WLS generally outperforms ML in terms of model fit and stability. In the first scenario with a sample size of 500, WLS achieves a lower average RMSEA (0.0141) compared to ML (0.0172). With a sample size of 1000 in the second scenario, both methods produce similar RMSEA values (0.009 for WLS and 0.0096 for ML), though WLS demonstrates lower variability. In the third scenario, also with a sample size of 1000, WLS records an average RMSEA of 0.0074 versus 0.0092 for ML. Overall, the results suggest that WLS is more effective and reliable than ML in providing accurate parameter estimates across different data conditions and sample sizes.
Deteksi Plagiarisme Dokumen Tugas Menggunakan Algoritma Shingling dan MD5 Fingerprint Chrisinta, Debora; Simarmata, Justin Eduardo; Purnomo, Miko; Santoso, Jaya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 1: Februari 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026131

Abstract

Masalah utama dalam lingkungan akademik adalah plagiarisme, yang dapat merusak reputasi institusi dan menghambat proses pembelajaran mahasiswa. Oleh karena itu, untuk mengatasi masalah ini diperlukan sistem deteksi plagiarisme yang efektif dan efisien. Tujuan dari penelitian ini adalah mengimplementasikan algoritma Shingling dan MD5 Fingerprint menggunakan Python untuk mendeteksi kemiripan teks dalam tugas mahasiswa. Data yang digunakan dalam penelitian ini berupa dokumen Word (.docx) berupa tugas akademik, seperti esai dan laporan, yang dikumpulkan dari mata kuliah Data Warehouse. Metode yang digunakan meliputi pra-pemrosesan teks, pembentukan shingle berbasis kata, dan perhitungan Jaccard Similarity untuk mengukur tingkat kemiripan antara dokumen. Hasil penelitian menunjukkan bahwa algoritma Shingling dan MD5 Fingerprint efektif dalam mendeteksi kemiripan teks, bahkan ketika terdapat variasi dalam struktur kalimat atau penggunaan bahasa. Hasil kemiripan divisualisasikan dalam grafik batang, yang menyajikan tingkat kemiripan antar dokumen secara jelas dan ringkas. Sistem ini diharapkan menjadi alat bantu andal bagi dosen dan institusi dalam memantau keaslian karya tulis mahasiswa secara real-time.   Abstract The main issue in the academic environment is plagiarism, which can damage the reputation of institutions and hinder the student learning process. Therefore, an effective and efficient plagiarism detection system is necessary to address this problem. The aim of this study is to implement the Shingling algorithm and MD5 Fingerprint using Python to detect text similarity in student assignments. The data used in this research consists of Word documents (.docx) of academic assignments, such as essays and reports, collected from the Data Warehouse course. The methods used include text preprocessing, word-based shingle formation, and Jaccard Similarity calculation to measure the similarity level between documents. The research results show that the Shingling algorithm and MD5 Fingerprint are effective in detecting text similarity, even when there are variations in sentence structure or language use. Visualization of the results in the form of graphs facilitates the identification of documents with high levels of plagiarism, allowing for further action to maintain academic integrity. This system is expected to be a reliable tool for lecturers and institutions to monitor the authenticity of student writings in real-time.
Evaluating interactive R-Shiny based mathematics learning media through motivation and engagement pathways in border-region schools Simarmata, Justin Eduardo; Purnomo, Miko; Fallo, Kristoforus
Journal on Mathematics Education Vol. 17 No. 1 (2026): Journal on Mathematics Education
Publisher : Universitas Sriwijaya in collaboration with Indonesian Mathematical Society (IndoMS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jme.v17i1.pp225-246

Abstract

Persistent disparities in mathematics learning outcomes between urban and border-area schools underscore enduring structural inequities linked to limited access to effective, contextually appropriate digital learning environments. Although technological integration in education has expanded substantially, empirical research examining the pedagogical effectiveness of interactive computational platforms—such as those developed using R-Shiny—remains limited, particularly in geographically marginalized regions. This study investigates the effectiveness of R-Shiny-based interactive mathematics learning media in enhancing students’ conceptual understanding and learning achievement. A quantitative research design employing a pretest–posttest approach was utilized, supported by validated questionnaires administered to 101 tenth-grade students enrolled in senior high schools along the Indonesia-Timor Leste border. Data were analyzed using Confirmatory Factor Analysis (CFA) to establish construct validity and Structural Equation Modeling (SEM) to test hypothesized relationships among learning motivation, student engagement, user satisfaction, conceptual understanding, and perceived academic performance. Descriptive statistics revealed consistent improvements in posttest scores relative to pretest results, signifying measurable gains in conceptual understanding. Moreover, SEM analysis indicated that learning motivation, student engagement, and user satisfaction each exerted positive and statistically significant effects on conceptual understanding, which in turn significantly predicted perceived academic performance. Collectively, these findings suggest that R-Shiny-based interactive media foster not only improved cognitive outcomes but also strengthen motivational and engagement-related processes that mediate mathematics learning in geographically disadvantaged educational settings.
Pengabdian kepada masyarakat melalui pengembangan modul interaktif untuk peningkatan kualitas pembelajaran statistika di SMP Satu Atap Negeri Maumolo Mone, Ferdinandus; Simarmata, Justin Eduardo; Kehi, Yohanes Jefrianus; Laja, Yosepha Patricia Wua; Purnomo, Miko; Chrisinta, Debora; Abi, Roberto; Tavares, Ricson
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i6.34518

Abstract

Abstrak Pembelajaran Statistika di tingkat SMP sering dianggap sulit oleh siswa karena penyajiannya yang cenderung abstrak dan kurang kontekstual. Kondisi ini juga dialami oleh siswa SMP Satu Atap Negeri Maumolo, khususnya di kelas VIII, yang menunjukkan rendahnya pemahaman konsep dasar Statistika. Oleh karena itu, kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan dan menerapkan modul interaktif sebagai media pembelajaran yang dapat meningkatkan pemahaman siswa. Mitra kegiatan adalah SMP Satu Atap Negeri Maumolo dengan sasaran 30 siswa kelas VIII yang mengikuti kegiatan selama dua hari, pada tanggal 1 dan 2 Agustus 2025. Metode pelaksanaan meliputi penyusunan modul interaktif, penyampaian materi berbasis kontekstual, diskusi, serta pendampingan dalam mengerjakan latihan soal. Hasil kegiatan menunjukkan peningkatan pemahaman siswa yang terlihat dari peningkatan rata-rata nilai latihan dari 58 pada awal kegiatan menjadi 80 setelah penggunaan modul. Secara kualitatif, siswa menyatakan modul mudah dipahami, tampilannya menarik, serta membantu meningkatkan kepercayaan diri dalam mempelajari Statistika. Lebih dari 85% siswa terlibat aktif dalam diskusi dan mampu menyelesaikan soal dengan baik. Dengan demikian, kegiatan ini memberikan dampak positif dalam mendukung peningkatan kualitas pembelajaran Statistika dan dapat dijadikan model alternatif pembelajaran bagi sekolah-sekolah di wilayah perbatasan. Kata kunci: Pengabdian masyarakat; modul interaktif; statistika; pemahaman siswa; SMP. Abstract Statistics learning at the junior high school level is often perceived as difficult by students due to its abstract presentation and lack of contextual approaches. This condition is also found among eighth-grade students of SMP Satu Atap Negeri Maumolo, who demonstrate limited understanding of basic statistical concepts. Therefore, this community service activity aimed to develop and implement an interactive module as a learning medium to improve students’ comprehension. The partner institution was SMP Satu Atap Negeri Maumolo, involving 30 eighth-grade students who participated in the program over two days, on August 1–2, 2025. The implementation methods included the development of the interactive module, contextual delivery of materials, group discussions, and guided practice in solving problems provided in the module. The results indicated an improvement in students’ understanding, as reflected in the increase of average practice scores from 58 at the beginning of the activity to 80 after the module was applied. Qualitatively, students reported that the module was easy to understand, visually engaging, and helpful in building their confidence in learning Statistics. More than 85% of students actively engaged in discussions and successfully completed the exercises. In conclusion, this activity had a positive impact on improving the quality of Statistics learning and can serve as an alternative model for schools in border regions. Keywords: community service; interactive module; statistics; student comprehension; junior high school.
Co-Authors Abi, Roberto Antonius Tuan Tana Ruron Atencia, Bryant Bennet D. Baldemor, Milagros R. Chrisinta, Debora Fallo, Kristoforus Fitra Ramadhan Hijriani, Lailin Iis Aprianti Jaya Santoso Jonris Tampubolon Kristofel Bere Nahak Matute, Alejandro Jr. V. mone, ferdinandus Simarmata, Justin Eduardo Tavares, Ricson Wua Laja, Yosepha Patricia Yohanes Jefrianus Kehi